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Biotech Has More Room to Run

Active Share: Not Necessary, and Definitely Not Sufficient

GDP: Getting Difficult to Predict

A Tale of Two Benchmarks: Benchmark Selection

Sector Dispersion and Active Management

Biotech Has More Room to Run

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Todd Rosenbluth

Director of ETF and Mutual Fund Research

S&P Capital IQ Equity Research

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Biotechnology has been one of the best performing industries in the stock market over the past several years. According to S&P Capital IQ, there were numerous catalysts, for this substantial stock outperformance, including several blockbuster drug approvals that drove significant sales and earnings growth. Yet, S&P CIQ thinks the industry’s drivers, including a robust pipeline, remain intact and have a positive fundamental outlook.

S&P Capital IQ equity analyst Jeff Loo expects approximately a dozen drugs to be approved and launched in 2015 with the potential to achieve blockbuster sales levels of more than $1 billion annually by their fifth year after launch (2020). Total sales for the seven biotech companies in the S&P 500 rose 41.5 % in 2014, driven by new drug approvals, and sales growth of 122%. In 2015, our forecasted rate of increase moderates to 13.2%, nonetheless, an impressive rate, in our view. Loo projects gross margin for those seven S&P 500 constituents, to widen to 89.8% in 2015, from 88.6% in 2014.

From January 1, 2011 to December 31, 2014, the S&P 1500 Biotechnology stock index rose 288.5% compared with the 63.6% rise for the S&P Composite 1500. As such, the S&P 1500 biotech industry’s valuation has expanded significantly from 12X forward 12-month EPS in 2011 to 18X in 2014. However, in spite of the significant multiple expansion, biotech’s current 19.1 multiple is equal the health care sector’s PE multiple of forward 12-months EPS and only slightly higher than the broader S&P 1500’s PE multiple of 18X, despite much stronger growth.

Investors have increasingly utilized ETFs in a tactical manner to gain exposure to industries, while benefitting from the ability to make intra-day trades and benefit from their low-cost, passive nature. In 2014, $41 billion was added to all sector ETFs, with nearly $6.4 billion in health care securities. In the first two months of 2015, health care products added an additional $3.0 billion of fresh money.

S&P Capital IQ has research and rankings on a number of ETFs that have meaningful exposure to the biotech industry. Visit http://trymsatoday.com/ to see the full article and/or view our ETF reports.
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S&P Dow Jones Indices is an independent provider of global indices, data and research and receives compensation for licensing its indices and other services to third parties. S&P Dow Indices does not sponsor, endorse, sell or promote any investment product or fund, nor make any investment recommendations. The views and opinions of any third party contributor are his/her own and may not necessarily represent the views or opinions of S&P Dow Jones Indices or any of its affiliates.

 

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Active Share: Not Necessary, and Definitely Not Sufficient

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Craig Lazzara

Managing Director and Global Head of Index Investment Strategy

S&P Dow Jones Indices

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The concept of active share was introduced several years ago as a measure of the degree to which a portfolio of stocks differs from its benchmark.  One of the intriguing results of the initial research on active share was that high active share managers seemed more likely to outperform than low active share managers.  This led, predictably enough, to a widespread belief that if active management wasn’t “working,” the solution was to be more aggressive, or as it was often expressed, to invest with more conviction.

At a basic level, there’s some obvious truth in this claim.  A manager with very low active share makes very small deviations from his benchmark index, and so can hardly be expected to generate large excess returns.  Similarly, a manager with high active share makes large deviations from his benchmark, which might lead to large differences in performance.  The difficulty is that those differences are no more likely to be positive than negative.

Consider: can an underperforming manager (of which there were plenty last year) improve his results by randomly selling half of his names, thus holding a more concentrated portfolio?  Doing so will increase active share for sure.  Is there any reason to believe that it will improve performance?  Of course not — which means that logically, it cannot be true that raising active share will enhance performance.  In that sense, high active share may be necessary, but it is clearly not sufficient.

Researchers at AQR Capital Management have recently argued that high active share is not necessary to produce attractive results.  Moreover, they show that the initial suggestion of a relationship between high active share and benchmark outperformance is due to a quirk in the data.  (High active share managers tended to be small cap managers, and small cap benchmarks had negative alphas relative to the entire equity market.  The high active share managers looked good because their benchmarks looked bad.)  Properly adjusted, AQR concludes that “there is no evidence you’re more likely to be right just because you have a high conviction.”

This in no sense eliminates the usefulness of the active share concept — it’s a handy cross-sectional measure of a manager’s aggressiveness.  It can help us frame reasonable expectations about differential performance.  What it can’t do is tell us whether those performance differentials will be positive or negative.  In the search for alpha, high active share may not be necessary, and is definitely not sufficient.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

GDP: Getting Difficult to Predict

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Jodie Gunzberg

Managing Director, Head of U.S. Equities

S&P Dow Jones Indices

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In the past 12 months, the S&P GSCI All Crude has lost almost 50% and has moved to the forefront of macroeconomic forces impacting the global economy. In the context of history, today’s oil price drop is not unprecedented in magnitude or duration as you can see in the chart below.GDP Energy history

In fact, there has been a comparable oil price drop every five ten years that could be blamed on a demand crisis or a flood of supply – or some combination of the two. The gold:oil ratio that represents how many barrels of oil it takes to buy an ounce of gold is also telling a story about a supply driven drop. The very high gold:oil ratio now is clearly driven by the oil drop whereas gold has maintained its value. If oil prices remain low versus gold for an extended period of time, as was the case in the 1986 period, the elevated gold:oil ratio may indicate that the energy production boom (whether U.S. or Saudi Arabia) is much more responsible for oil’s price collapse than fears of global deflation, lack of demand, and recession.

Back then, oil prices had been stable at historically high levels of $30/bbl for nearly three years from 1983-1985 that resulted in weak demand and strong non-OPEC production growth. That drove OPEC to flood the market with oil in order to maintain market share rather than support prices. The result was oil prices collapsed to $10/bbl within 8 months. Notice in the graph below that superimposes today’s oil price on the 1985-1986 graph where the pre-crisis shows elevated oil prices to the left of the black line and the drop is to the right of the black line:GDP 86Brent

The similarity is clear and these conditions seem to be in place again today.  The Oil Market Report (OMR) by the International Energy Agency stated oil price pressure in March was due to sharply higher supplies from Middle East OPEC producers, recording its highest month-on-month gain in nearly four years, despite a relentless build in US crude.

The big question that is weighing on analysts is about how the oil price drop is impacting the economy today. A helpful chart by Blackrock shows the incremental GDP impact from oil at $50/bbl as seen below where the green countries benefit and the blue countries lose:GDP 50 Oil

The winners and losers are divided by oil producers, importers and country specific policies. That seems fairly straightforward, but the oil economics are on a slippery slope right now as evidenced by the frequency and magnitude of recent GDP growth forecast changes. In just the first two months of the year Deutsche Bank had already made major changes to its global markets and economic forecasts because the markets were moving much more quickly than anticipated in response to lower oil, deflationary pressures, and a litany of unexpected central bank policy changes. In the chart below, DB illustrates the world GDP growth divided by large oil producers and other countries, and circled in red/green are big downward/upward revisions.DB GDP 2015

Since oil has become a major macroeconomic factor and a key input to support GDP growth, but is currently is hard to predict, GDP growth is also uncertain. Key factors that may influence the oil price going forward are the effects of capital expenditure cuts in 2015, geopolitical risks, possible stagnation in Europe and Japan and production decisions by OPEC. If oil prices stabilize near current levels, then credit risk may increase causing assets to shift based on Fed actions like in the risk-on risk off environment post the global financial crisis. Given our economy is still in a post-global financial transition, it is highly sensitive to these factors.

According to the OMR, recent developments may drive more uncertainty about the supply and demand responses. Stronger-than-expected 1Q15 demand might signal a faster recovery – as would a faster-than-expected decline in North American unconventional supply. However, there may be a slower recovery if pockets of demand strength prove short-lived and lead to weaker deliveries later on. Geopolitics further call into question past working assumptions on future output, but may already have encouraged some producers to hike supply to stake out market share ahead of possible lifting sanctions. All in all, that suggests the market rebalancing may still be in its early stage.

One area of agreement in a world divided by oil importers, exporters, developed and emerging countries is there is an opportunity for energy reform from the oil price drop despite policy differences. Oil importers may benefit from the savings from the removal of energy subsidies that may be used to lower budget deficits and to increase public infrastructure. Developed economies may enjoy a boost to demand from the oil price drop but more monetary policy may be needed to prevent real interest rates from rising, especially if there are further declines in inflation. Infrastructure investment may help the need to support the recovery and long-term growth in these countries. On the other hand, macroeconomic policy to support growth remains limited in emerging markets, but in some cases, lower oil prices may alleviate inflation pressure and external vulnerabilities, thereby allowing central banks not to raise policy interest rates or to raise them more gradually. Some oil exporters may need to strengthen their monetary frameworks to avert the possibility that depreciation will lead to persistently higher inflation and further depreciation.

 

 

The posts on this blog are opinions, not advice. Please read our Disclaimers.

A Tale of Two Benchmarks: Benchmark Selection

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Phillip Brzenk

Senior Director, Strategy Indices

S&P Dow Jones Indices

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This is the fourth post in a series of blog posts relating to the in-depth analysis of performance differential between the S&P SmallCap 600® and the Russell 2000.

The previous posts demonstrate that the different historical risk/return profiles of the two U.S. small-cap benchmarks can be partially explained by the July reconstitution effect and the additional S&P DJI screening criteria.  The difference in returns between the two indices highlights the fact that investors should be aware that index construction differences can have a meaningful impact on returns.  Both indices represent a particular market segment which, in turn, poses practical considerations for both passive and active investors who employ index returns as a key decision input in the investment process.

For those tasked with evaluating managers, conclusions about the ability of a manager to add value can vary depending on which benchmark is used in the evaluation.  In that light, we looked at the impact of benchmark selection in the performance measurement process.  In order to determine the effect that selecting one of the small-cap indices can have in evaluation, a universe of actively managed U.S. small-cap funds were compared against the indices.  Exhibits 1 and 2 show the percentage of funds that underperformed each benchmark based on rolling three- and five-year returns, respectively, on a semiannual basis from 2005 through 2014.  Based on the three-year annualized returns, approximately 73% of funds underperformed the S&P SmallCap 600 on average, while approximately 60% underperformed the Russell 2000 over the same period.  Results are similar looking at the five-year annualized returns, where approximately 73% underperformed the S&P SmallCap 600 and approximately 59% underperformed the Russell 2000.

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These results highlight the difficulty investors sometimes face in measuring the value offered by active managers.  If different benchmarks measuring the same asset class can yield different realized returns, the ability to differentiate a skilled manager from an unskilled one can be an arduous process.  We now look at the information ratio (IR), defined as the active return divided by active risk, to measure the effectiveness of a manager’s investment insight, irrespective of the benchmark against which he or she is being measured.

To do this, we calculate the IRs of the active small-cap funds against the two benchmarks.  With active funds in the Lipper Small-Cap Core Fund category as the universe, the average IR using rolling three-year annualized returns is computed on a quarterly basis from December 1996 through December 2014.  We see that there is a noticeable difference in the average IR of the category when the S&P SmallCap 600 is used as the benchmark compared with when the Russell 2000 is used.  The average of the three-year rolling IRs is negative for the universe compared to the S&P SmallCap 600 (IR = -0.24), while it is positive when compared to the Russell (IR = 0.25).

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The rolling three-year information ratios show that the S&P SmallCap 600 is almost always the tougher benchmark to beat for active managers.  Investors may want to consider that the selection of a benchmark matters when it comes to benchmarking domestic small-cap equities.

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The posts on this blog are opinions, not advice. Please read our Disclaimers.

Sector Dispersion and Active Management

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Craig Lazzara

Managing Director and Global Head of Index Investment Strategy

S&P Dow Jones Indices

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Market volatility is a function of both dispersion and correlation, as shown in this schematic:

Dispersion and correlation 2Dispersion measures the degree to which the components of an index perform similarly.  If the components are tightly bunched, dispersion will be low and, other things equal, the index’s volatility will be low.  Correlation is a measure of timing; it measures the tendency of index components to rise or fall at the same time.  If the components tend to move together, correlation will be relatively high, and volatility will rise.  If component moves tend to offset, correlation and volatility will be lower.  In terms of our simple schematic, the farther from the origin an index is, the higher its volatility will be.

Dispersion, correlation, and volatility can be measured at the market index level, of course, but can equally well be measured at a finer level of granularity.  Below we’ve graphed these metrics for each of the 10 sectors of the S&P 500:

S&P 500 sector dispersion and correlationWe immediately notice that the highest volatility sectors tend to be the farthest from the origin and the lowest volatility sectors the closest — confirming our intuition about the interaction of dispersion and correlation.  But these data — ironically, perhaps, derived entirely from passive benchmarks — can also provide some useful guidance for active investors.

First, since dispersion measures the potential benefit of stock selection, an active stock picker might wish to concentrate his efforts on high-dispersion sectors.  There is, e.g., more potential benefit to choosing among technology stocks than among energy companies or utilities.  If analytic resources are scarce, in fact, there’s an argument to be made for simply indexing the low-dispersion sectors.  (We know at least one major institutional investor which does exactly that.)

Second, the nature of the most relevant analytic input differs across sectors.  For low-dispersion, high-correlation sectors, the most important decision is the sector call, not individual stock recommendations.  The returns of the constituents of these sectors tend to cluster relatively tightly, so stock selection is of relatively little value.  On the other hand, where correlations are high, it means that most stocks in the sector move up and down together.  A correct sector call will be reflected more consistently across all sector components.

An analyst who follows utilities or energy would be well advised to spend most of his time and effort deciding whether to be in or out of the sector.  An analyst who follows technology or healthcare may be better off trying to separate the sectoral wheat from the sectoral chaff.  Dispersion and correlation not only provide insight into the volatility of sector returns, but offer guidance for active analysts as well.

The posts on this blog are opinions, not advice. Please read our Disclaimers.